Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "36" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 28 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 28 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460011 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.423899 | 8.795114 | 1.179919 | 0.680645 | 3.079052 | 3.790193 | 0.336763 | 1.021857 | 0.5636 | 0.5671 | 0.3768 | nan | nan |
| 2460010 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.645383 | 9.382311 | 0.980091 | 0.936170 | 2.086414 | 2.701966 | 0.233632 | 0.604378 | 0.5739 | 0.5749 | 0.3826 | nan | nan |
| 2460009 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.252017 | 9.181013 | 1.210852 | 1.024888 | 1.722525 | 2.090109 | -0.087493 | 1.036898 | 0.5806 | 0.5858 | 0.3857 | nan | nan |
| 2460008 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.803511 | 10.617940 | 1.380723 | 1.166532 | 0.960013 | 1.692781 | 3.748891 | 4.487980 | 0.6229 | 0.6296 | 0.3456 | nan | nan |
| 2460007 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.206913 | 8.210306 | 1.226203 | 1.103589 | 0.673251 | 1.947535 | 0.168519 | 1.057571 | 0.5835 | 0.5913 | 0.3705 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 99.92% | 99.92% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.1305 | 0.1254 | 0.0962 | nan | nan |
| 2459998 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.134386 | 7.780480 | 1.036067 | 0.871462 | 2.579223 | 2.946483 | -0.344675 | 0.578063 | 0.5791 | 0.5925 | 0.4020 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.407764 | 8.235790 | 1.046347 | 1.013068 | 1.518055 | 2.094114 | 0.145516 | 1.166367 | 0.5936 | 0.6095 | 0.4022 | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.109396 | 9.104376 | 1.594950 | 1.166811 | 1.058356 | 1.746824 | 0.223589 | 0.547498 | 0.6000 | 0.6130 | 0.4148 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 28.307166 | 28.474151 | 15.421443 | 15.422246 | 8.256972 | 9.508937 | 3.252391 | 3.288799 | 0.0361 | 0.0327 | 0.0018 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 27.201309 | 27.568368 | 13.353835 | 13.508881 | 7.813669 | 9.427208 | 2.953349 | 2.954699 | 0.0317 | 0.0294 | 0.0017 | nan | nan |
| 2459993 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 30.136385 | 29.299697 | 12.544392 | 12.635282 | 10.300395 | 10.866794 | 3.303072 | 4.214048 | 0.0263 | 0.0248 | 0.0018 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 31.451010 | 31.790927 | 13.206975 | 13.310546 | 9.272035 | 10.650294 | 2.975776 | 3.082472 | 0.0311 | 0.0286 | 0.0017 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 25.556794 | 26.096683 | 12.963174 | 12.976029 | 9.201618 | 10.972596 | 5.142407 | 4.999926 | 0.0337 | 0.0312 | 0.0017 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 25.695317 | 26.625609 | 11.554330 | 11.813025 | 8.150387 | 9.215716 | 4.569390 | 4.540998 | 0.0307 | 0.0283 | 0.0015 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 30.595486 | 31.365549 | 13.405425 | 13.371059 | 10.832781 | 13.034771 | 2.549341 | 2.565166 | 0.0301 | 0.0280 | 0.0017 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 25.862520 | 26.492850 | 12.959316 | 13.114866 | 6.440103 | 7.877463 | 5.448925 | 5.749940 | 0.0329 | 0.0306 | 0.0016 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 31.435673 | 32.008882 | 14.175789 | 14.181677 | 9.478261 | 11.171645 | 7.799028 | 11.291118 | 0.0319 | 0.0294 | 0.0017 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 29.605847 | 29.887344 | 13.125967 | 13.190686 | 7.319815 | 8.527010 | 7.436144 | 7.180107 | 0.0314 | 0.0292 | 0.0014 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 28.420057 | 28.528233 | 13.591203 | 13.644808 | 9.394766 | 11.802545 | 5.612791 | 5.849313 | 0.0339 | 0.0314 | 0.0015 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 27.055249 | 27.435414 | 13.051777 | 12.979509 | 9.408699 | 11.073044 | 6.190692 | 8.753462 | 0.0328 | 0.0304 | 0.0014 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.067248 | 12.025065 | 11.096671 | 11.078569 | 4.719831 | 5.424723 | 2.556650 | 3.409739 | 0.0318 | 0.0295 | 0.0011 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 24.705361 | 25.066399 | 13.989848 | 13.881792 | 10.559121 | 12.222765 | 4.076620 | 4.263589 | 0.0338 | 0.0315 | 0.0015 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 24.486300 | 24.754669 | 12.541030 | 12.615748 | 9.168726 | 10.703282 | 5.775387 | 5.899386 | 0.0333 | 0.0309 | 0.0013 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 25.167496 | 25.390291 | 11.738379 | 11.884480 | 9.069760 | 10.028516 | 4.745504 | 4.876806 | 0.0337 | 0.0293 | 0.0018 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 25.500538 | 25.863164 | 12.724740 | 12.799018 | 9.458004 | 10.865468 | 3.983408 | 4.141558 | 0.0295 | 0.0274 | 0.0015 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 26.071356 | 26.654449 | 12.392331 | 12.513395 | 9.363009 | 11.178968 | 4.276260 | 4.486964 | 0.0343 | 0.0319 | 0.0015 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 25.916891 | 26.208022 | 13.093890 | 13.108701 | 9.560374 | 10.763877 | 3.205233 | 3.063496 | 0.0308 | 0.0283 | 0.0016 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 8.795114 | 7.423899 | 8.795114 | 1.179919 | 0.680645 | 3.079052 | 3.790193 | 0.336763 | 1.021857 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | ee Shape | 9.645383 | 9.645383 | 9.382311 | 0.980091 | 0.936170 | 2.086414 | 2.701966 | 0.233632 | 0.604378 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | ee Shape | 9.252017 | 9.252017 | 9.181013 | 1.210852 | 1.024888 | 1.722525 | 2.090109 | -0.087493 | 1.036898 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | ee Shape | 10.803511 | 10.617940 | 10.803511 | 1.166532 | 1.380723 | 1.692781 | 0.960013 | 4.487980 | 3.748891 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 8.210306 | 7.206913 | 8.210306 | 1.226203 | 1.103589 | 0.673251 | 1.947535 | 0.168519 | 1.057571 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 7.780480 | 7.134386 | 7.780480 | 1.036067 | 0.871462 | 2.579223 | 2.946483 | -0.344675 | 0.578063 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 8.235790 | 7.407764 | 8.235790 | 1.046347 | 1.013068 | 1.518055 | 2.094114 | 0.145516 | 1.166367 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 9.104376 | 8.109396 | 9.104376 | 1.594950 | 1.166811 | 1.058356 | 1.746824 | 0.223589 | 0.547498 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 28.474151 | 28.307166 | 28.474151 | 15.421443 | 15.422246 | 8.256972 | 9.508937 | 3.252391 | 3.288799 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 27.568368 | 27.201309 | 27.568368 | 13.353835 | 13.508881 | 7.813669 | 9.427208 | 2.953349 | 2.954699 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | ee Shape | 30.136385 | 30.136385 | 29.299697 | 12.544392 | 12.635282 | 10.300395 | 10.866794 | 3.303072 | 4.214048 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 31.790927 | 31.451010 | 31.790927 | 13.206975 | 13.310546 | 9.272035 | 10.650294 | 2.975776 | 3.082472 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 26.096683 | 26.096683 | 25.556794 | 12.976029 | 12.963174 | 10.972596 | 9.201618 | 4.999926 | 5.142407 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 26.625609 | 26.625609 | 25.695317 | 11.813025 | 11.554330 | 9.215716 | 8.150387 | 4.540998 | 4.569390 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 31.365549 | 31.365549 | 30.595486 | 13.371059 | 13.405425 | 13.034771 | 10.832781 | 2.565166 | 2.549341 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 26.492850 | 25.862520 | 26.492850 | 12.959316 | 13.114866 | 6.440103 | 7.877463 | 5.448925 | 5.749940 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 32.008882 | 32.008882 | 31.435673 | 14.181677 | 14.175789 | 11.171645 | 9.478261 | 11.291118 | 7.799028 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 29.887344 | 29.887344 | 29.605847 | 13.190686 | 13.125967 | 8.527010 | 7.319815 | 7.180107 | 7.436144 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 28.528233 | 28.420057 | 28.528233 | 13.591203 | 13.644808 | 9.394766 | 11.802545 | 5.612791 | 5.849313 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 27.435414 | 27.055249 | 27.435414 | 13.051777 | 12.979509 | 9.408699 | 11.073044 | 6.190692 | 8.753462 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | ee Shape | 13.067248 | 13.067248 | 12.025065 | 11.096671 | 11.078569 | 4.719831 | 5.424723 | 2.556650 | 3.409739 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 25.066399 | 25.066399 | 24.705361 | 13.881792 | 13.989848 | 12.222765 | 10.559121 | 4.263589 | 4.076620 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 24.754669 | 24.754669 | 24.486300 | 12.615748 | 12.541030 | 10.703282 | 9.168726 | 5.899386 | 5.775387 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 25.390291 | 25.167496 | 25.390291 | 11.738379 | 11.884480 | 9.069760 | 10.028516 | 4.745504 | 4.876806 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 25.863164 | 25.863164 | 25.500538 | 12.799018 | 12.724740 | 10.865468 | 9.458004 | 4.141558 | 3.983408 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 26.654449 | 26.071356 | 26.654449 | 12.392331 | 12.513395 | 9.363009 | 11.178968 | 4.276260 | 4.486964 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | N03 | RF_maintenance | nn Shape | 26.208022 | 26.208022 | 25.916891 | 13.108701 | 13.093890 | 10.763877 | 9.560374 | 3.063496 | 3.205233 |